Comparison of Grizzly Bear <i>Ursus arctos</i> Demographics in Wilderness Mountains Versus a Plateau with Resource Development
Bibliographic record
Abstract
We studied migration, birth rate and death rate of 59 grizzly bears Ursus arctos on a plateau (N = 29) with extensive forestry activities, and adjacent relatively undeveloped mountains (N = 30) during 1998–2003, to examine population parameters and/or limiting factors that might have contributed to a large difference in grizzly bear density between landscapes. Female bears in our low‐density area (i.e. plateau) were heavier, in better condition, and more often accompanied by cubs than their mountain counterparts. Survival rates were comparable for adult bears but were significantly lower for subadult bears on the plateau. All deaths of bears which lived on the plateau for which cause of death was identified were human‐caused as compared with one of three in the mountains. Plateau bear deaths were highest in fall coinciding with people hunting other species. Density‐dependent factors appeared to be more important to mountain bear demographics, whereas bears on the plateau appeared to be limited by human‐caused bear mortality. Forest harvest did not appear to have negative effects on reproductive parameters of female bears, but associated towns and roads allowed for high human‐caused bear mortality. We did not record female dispersal between landscapes leading us to conclude that dispersal from the mountains is unlikely to offset human‐caused mortality on the plateau.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".